Soil organic matter (SOM) is a key indicator of soil fertility and ecosystem resilience, yet its spatial and vertical variation mechanisms remain poorly understood in fragile mountainous landscapes. This study integrates machine learning and interpretable causal modeling to elucidate the multiscale drivers of SOM distribution in the Yihe River Basin (YRB), a typical erosion‐prone region in northern China. The Random Forest (RF) model was used to predict the spatial distribution of SOM content, and the relative contributions and pathways of natural and anthropogenic factors were identified using the Piecewise structural equation (PSEM) model and Shapley additive explanations (SHAP). Results showed that the RF model successfully predicted SOM content with coefficients of determination ( R2 ) of 0.695, 0.671, 0.626, and 0.730 for the four soil horizons, respectively. Horizontally, SOM content was higher in the eastern and northern regions of the basin. Vertically, the SOM content showed a decreasing trend with increasing soil depth. Among the factors influencing SOM distribution, soil properties, particularly total nitrogen content, were identified as the most significant determinants. Moreover, human activities primarily influence SOM indirectly by modifying soil physicochemical properties and climatic conditions. Their effects are more pronounced in surface soils, with total impact effects ranging from 0.94 to 1.04. These findings provide new insights into SOM dynamics in fragile landscapes and offer scientific guidance for targeted soil management and ecological restoration in erosion‐prone regions.
{"title":"Soil Organic Matter in Rocky Mountainous Area of Northern China: Spatial Distribution, Drivers and Mechanisms","authors":"Yingzi Li, Jinkuo Lin, Shuwei Zheng, Zijun Li, Zhichao Wang, Jiakang Chen","doi":"10.1002/ldr.70320","DOIUrl":"https://doi.org/10.1002/ldr.70320","url":null,"abstract":"Soil organic matter (SOM) is a key indicator of soil fertility and ecosystem resilience, yet its spatial and vertical variation mechanisms remain poorly understood in fragile mountainous landscapes. This study integrates machine learning and interpretable causal modeling to elucidate the multiscale drivers of SOM distribution in the Yihe River Basin (YRB), a typical erosion‐prone region in northern China. The Random Forest (RF) model was used to predict the spatial distribution of SOM content, and the relative contributions and pathways of natural and anthropogenic factors were identified using the Piecewise structural equation (PSEM) model and Shapley additive explanations (SHAP). Results showed that the RF model successfully predicted SOM content with coefficients of determination ( <jats:italic>R</jats:italic> <jats:sup>2</jats:sup> ) of 0.695, 0.671, 0.626, and 0.730 for the four soil horizons, respectively. Horizontally, SOM content was higher in the eastern and northern regions of the basin. Vertically, the SOM content showed a decreasing trend with increasing soil depth. Among the factors influencing SOM distribution, soil properties, particularly total nitrogen content, were identified as the most significant determinants. Moreover, human activities primarily influence SOM indirectly by modifying soil physicochemical properties and climatic conditions. Their effects are more pronounced in surface soils, with total impact effects ranging from 0.94 to 1.04. These findings provide new insights into SOM dynamics in fragile landscapes and offer scientific guidance for targeted soil management and ecological restoration in erosion‐prone regions.","PeriodicalId":203,"journal":{"name":"Land Degradation & Development","volume":"14 1","pages":""},"PeriodicalIF":4.7,"publicationDate":"2025-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145593004","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Dissolved organic matter (DOM) plays a critical role in soil biogeochemical processes and affects the responses of soil organic matter (SOM) to agricultural practices. However, the effect of converting forests to intensive orchards on the chemodiversity of soil DOM remains poorly understood. In this study, we systematically investigated molecular‐scale transformations in soil DOM resulting from the conversion of Chinese fir ( Cunninghamia lanceolata [Lamb.] Hook) plantations to citrus orchards of varying ages (10, 30, and 50 years) using an integrative approach combining UV–Vis spectroscopy, EEM‐PARAFAC, and FT‐ICR MS. Long‐term citrus cultivation promoted the stabilization of DOM, which was associated with increased aromaticity (SUVA 524 , SUVA 280 ), oxidation (O/C wa : 0.440–0.566), and molecular weight (m/z wa : 395.977–413.460), alongside decreased aliphaticity (H/C wa : 1.368–1.023). In 50‐year orchards, recalcitrant compounds dominated the DOM pool (87.7% vs. an initial 77.9%), and the abundance of combustion‐derived polycyclic aromatics (+1292%) and lignin derivatives (from 2204 to 4485 molecules) increased. In contrast, microbial‐derived components (e.g., proteins and carbohydrates) declined substantially with prolonged cultivation. Soil acidification (pH 5.42–4.08) and organic fertilization jointly enhanced the formation of aromatic‐condensed DOM structures, with the 30‐year stage marking a critical transition in humification processes. These findings underscore the role of agricultural intensification in shaping soil carbon persistence and provide molecular‐level insights to inform carbon sequestration strategies in managed ecosystems.
{"title":"Interdecadal Dynamics in the Chemodiversity of Soil Dissolved Organic Matter After the Conversion of Chinese Fir Plantations to Citrus Orchard","authors":"Junquan Chen, Yanqi Guo, Xinyue Ma, Limin Lan, Guanjie Jiang, Peng Long, Taihui Zheng, Qin Zhang","doi":"10.1002/ldr.70323","DOIUrl":"https://doi.org/10.1002/ldr.70323","url":null,"abstract":"Dissolved organic matter (DOM) plays a critical role in soil biogeochemical processes and affects the responses of soil organic matter (SOM) to agricultural practices. However, the effect of converting forests to intensive orchards on the chemodiversity of soil DOM remains poorly understood. In this study, we systematically investigated molecular‐scale transformations in soil DOM resulting from the conversion of Chinese fir ( <jats:styled-content style=\"fixed-case\"> <jats:italic>Cunninghamia lanceolata</jats:italic> </jats:styled-content> [Lamb.] Hook) plantations to citrus orchards of varying ages (10, 30, and 50 years) using an integrative approach combining UV–Vis spectroscopy, EEM‐PARAFAC, and FT‐ICR MS. Long‐term citrus cultivation promoted the stabilization of DOM, which was associated with increased aromaticity (SUVA <jats:sub>524</jats:sub> , SUVA <jats:sub>280</jats:sub> ), oxidation (O/C <jats:sub>wa</jats:sub> : 0.440–0.566), and molecular weight (m/z <jats:sub>wa</jats:sub> : 395.977–413.460), alongside decreased aliphaticity (H/C <jats:sub>wa</jats:sub> : 1.368–1.023). In 50‐year orchards, recalcitrant compounds dominated the DOM pool (87.7% vs. an initial 77.9%), and the abundance of combustion‐derived polycyclic aromatics (+1292%) and lignin derivatives (from 2204 to 4485 molecules) increased. In contrast, microbial‐derived components (e.g., proteins and carbohydrates) declined substantially with prolonged cultivation. Soil acidification (pH 5.42–4.08) and organic fertilization jointly enhanced the formation of aromatic‐condensed DOM structures, with the 30‐year stage marking a critical transition in humification processes. These findings underscore the role of agricultural intensification in shaping soil carbon persistence and provide molecular‐level insights to inform carbon sequestration strategies in managed ecosystems.","PeriodicalId":203,"journal":{"name":"Land Degradation & Development","volume":"90 1","pages":""},"PeriodicalIF":4.7,"publicationDate":"2025-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145594151","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Constructing and optimizing an ecological security pattern (ESP) is a critical approach to safeguarding ecosystem health and promoting sustainable development, particularly in rapidly developing cities. However, traditional research methods often fail to accurately and effectively construct complex and dynamic ESPs. This limitation poses considerable challenges to the formulation of ecological restoration strategies and the maintenance of ecosystem stability. To address these challenges, the present study innovatively introduces an integrated methodological framework that combines the remote sensing ecological index (RSEI), morphological spatial pattern analysis (MSPA), landscape connectivity index (LCI), machine learning algorithms and circuit theory to construct the ESP of Kunming from 2000 to 2021. The framework is centered on the three elements ‘ecological source’, ‘ecological corridor’ and ‘ecological pinch point’, and further proposes targeted optimization strategies. Results demonstrate the substantial applicability and advantages of machine learning algorithms in ESP construction, and the XGBoost model emerging as the most effective approach for developing ecological resistance surfaces. Between 2000 and 2021, Kunming's ecological source area expanded from 882.90 to 1424.87 km 2 , forming a spatial pattern characterized by clustered distributions in the west and north and scattered distribution in the east. The total length of ecological corridors increased from 1141.28 to 1553.98 km, the number of connected corridors from 39 to 72 and ecological pinch points from 22 to 26. These changes have effectively mitigated ecological fragmentation, facilitated species migration and supported habitat restoration. Additionally, the number of ecological barriers grew from 21 to 31, particularly in the vicinity of Dianchi Lake. Given the obstructive impact of urbanization on the ecological network in this region, enhanced ecological protection and measures to restore network connectivity are imperative. Based on these findings, an optimized ESP framework, referred to as the ‘four zones and three belts’ model, was developed for Kunming city. This framework provides valuable theoretical and methodological insights for the construction and optimization of ESP in urban contexts.
{"title":"Construction and Optimization of Ecological Security Pattern in Kunming: Insights From Machine Learning Algorithms and Circuit Theory","authors":"Lin Zhang, Yutong Li, Kanli Wei, Shi Qi","doi":"10.1002/ldr.70330","DOIUrl":"https://doi.org/10.1002/ldr.70330","url":null,"abstract":"Constructing and optimizing an ecological security pattern (ESP) is a critical approach to safeguarding ecosystem health and promoting sustainable development, particularly in rapidly developing cities. However, traditional research methods often fail to accurately and effectively construct complex and dynamic ESPs. This limitation poses considerable challenges to the formulation of ecological restoration strategies and the maintenance of ecosystem stability. To address these challenges, the present study innovatively introduces an integrated methodological framework that combines the remote sensing ecological index (RSEI), morphological spatial pattern analysis (MSPA), landscape connectivity index (LCI), machine learning algorithms and circuit theory to construct the ESP of Kunming from 2000 to 2021. The framework is centered on the three elements ‘ecological source’, ‘ecological corridor’ and ‘ecological pinch point’, and further proposes targeted optimization strategies. Results demonstrate the substantial applicability and advantages of machine learning algorithms in ESP construction, and the XGBoost model emerging as the most effective approach for developing ecological resistance surfaces. Between 2000 and 2021, Kunming's ecological source area expanded from 882.90 to 1424.87 km <jats:sup>2</jats:sup> , forming a spatial pattern characterized by clustered distributions in the west and north and scattered distribution in the east. The total length of ecological corridors increased from 1141.28 to 1553.98 km, the number of connected corridors from 39 to 72 and ecological pinch points from 22 to 26. These changes have effectively mitigated ecological fragmentation, facilitated species migration and supported habitat restoration. Additionally, the number of ecological barriers grew from 21 to 31, particularly in the vicinity of Dianchi Lake. Given the obstructive impact of urbanization on the ecological network in this region, enhanced ecological protection and measures to restore network connectivity are imperative. Based on these findings, an optimized ESP framework, referred to as the ‘four zones and three belts’ model, was developed for Kunming city. This framework provides valuable theoretical and methodological insights for the construction and optimization of ESP in urban contexts.","PeriodicalId":203,"journal":{"name":"Land Degradation & Development","volume":"55 1","pages":""},"PeriodicalIF":4.7,"publicationDate":"2025-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145594150","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Saline soils represent a critical global ecological and environmental challenge. The precise and rapid determination of total salt content in saline soils during freezing–thawing processes is vital to effectively manage and mitigate soil salinization. This study builds on a volumetric mixing permittivity model for frozen soils, which accounts for the microstructure of saline soils during freezing and the occurrence of salts in either dissolved or crystalline states. A novel method is proposed to quantitatively invert the total salt content by integrating theoretical modeling with experimental validation. This approach extends the application of widely used multi‐parameter sensors, such as 5TE, by incorporating dielectric constant, electrical conductivity, and temperature as input parameters, thereby enabling the determination of total salt content in saline soils. Experimental results revealed that the contents of soluble and crystalline salts exhibited stage‐dependent variations during the freezing–thawing process. The mutual transformation between soluble and crystalline salts was jointly regulated by the initial water and salt conditions, reflecting a dynamic equilibrium between dissolution and crystallization. The inversion results of the model showed strong agreement with the experimental data, confirming the reliability and applicability of the method. This study proposes a method capable of accurately inverting the total salt content in saline soils at any temperature during the freezing–thawing process. This method effectively addresses the challenge of real‐time monitoring of total salt content during soil freezing–thawing processes. Future research should assess the applicability of the model across diverse soil types and field conditions, and promote its integration into dynamic salinity monitoring and precision agriculture.
{"title":"A Novel Method for Inverting Total Salt Content in Soil During Freezing–Thawing Process","authors":"Yan Song, Mingtang Chai, Jiashuang Yang, Wangcheng Li, Jiayi Zhang, Jianing Feng","doi":"10.1002/ldr.70267","DOIUrl":"https://doi.org/10.1002/ldr.70267","url":null,"abstract":"Saline soils represent a critical global ecological and environmental challenge. The precise and rapid determination of total salt content in saline soils during freezing–thawing processes is vital to effectively manage and mitigate soil salinization. This study builds on a volumetric mixing permittivity model for frozen soils, which accounts for the microstructure of saline soils during freezing and the occurrence of salts in either dissolved or crystalline states. A novel method is proposed to quantitatively invert the total salt content by integrating theoretical modeling with experimental validation. This approach extends the application of widely used multi‐parameter sensors, such as 5TE, by incorporating dielectric constant, electrical conductivity, and temperature as input parameters, thereby enabling the determination of total salt content in saline soils. Experimental results revealed that the contents of soluble and crystalline salts exhibited stage‐dependent variations during the freezing–thawing process. The mutual transformation between soluble and crystalline salts was jointly regulated by the initial water and salt conditions, reflecting a dynamic equilibrium between dissolution and crystallization. The inversion results of the model showed strong agreement with the experimental data, confirming the reliability and applicability of the method. This study proposes a method capable of accurately inverting the total salt content in saline soils at any temperature during the freezing–thawing process. This method effectively addresses the challenge of real‐time monitoring of total salt content during soil freezing–thawing processes. Future research should assess the applicability of the model across diverse soil types and field conditions, and promote its integration into dynamic salinity monitoring and precision agriculture.","PeriodicalId":203,"journal":{"name":"Land Degradation & Development","volume":"20 1","pages":""},"PeriodicalIF":4.7,"publicationDate":"2025-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145583036","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Maoyuan Sun, Dengpan Xiao, Yang Lu, Huizi Bai, Peipei Pan, Man Zhang, Xiaomeng Yin, Haonan Tan, Chen Wang
Understanding agricultural land systems transformation is vital for food security, ecology, and rural development. This study analyzes spatiotemporal dynamics in the North China Plain (2002–2022), integrating environmental constraints, economic incentives, and policy interventions. By fusing MODIS (250 m) and Landsat (30 m) data via random forest, we generated high‐accuracy crop distribution maps for 4 years (2002, 2012, 2017, 2022), enhancing resolution and overcoming cloud limitations. The key transformations occurred across the main systems: winter wheat–summer maize (WW‐SM) proportion increased (34.4%–37.8%) but absolute area declined by 18.7% (46,233–37,603 km 2 ), shifting from water‐stressed Central Hebei to favorable regions in Northern Henan and Northwestern Shandong. Single maize expanded 10.8% (12,644–14,011 km 2 ), concentrated in groundwater‐depleted zones like Cangzhou, aligning with drought adaptation. Cotton cultivation plummeted (14.1%–3.3%) due to labor intensity and low profitability. Fruit/forest surged 164%, driven by policies and markets, whereas vegetables fragmented near urban areas. Transition analysis revealed 18% of WW‐SM areas converted to fruit/forest and vegetables during 2012–2017, coinciding with China's 2014 Groundwater Overdraft Control Regulation. Although adaptive strategies have mitigated water stress, persistent spatial mismatches in water allocation remain. Our integrated framework demonstrates robust capacity for agricultural landscape monitoring, providing actionable insights for the water‐energy‐food nexus.
{"title":"Spatiotemporal Evolution of Agricultural Land Systems in the North China Plain Over Two Decades (2002–2022)","authors":"Maoyuan Sun, Dengpan Xiao, Yang Lu, Huizi Bai, Peipei Pan, Man Zhang, Xiaomeng Yin, Haonan Tan, Chen Wang","doi":"10.1002/ldr.70316","DOIUrl":"https://doi.org/10.1002/ldr.70316","url":null,"abstract":"Understanding agricultural land systems transformation is vital for food security, ecology, and rural development. This study analyzes spatiotemporal dynamics in the North China Plain (2002–2022), integrating environmental constraints, economic incentives, and policy interventions. By fusing MODIS (250 m) and Landsat (30 m) data via random forest, we generated high‐accuracy crop distribution maps for 4 years (2002, 2012, 2017, 2022), enhancing resolution and overcoming cloud limitations. The key transformations occurred across the main systems: winter wheat–summer maize (WW‐SM) proportion increased (34.4%–37.8%) but absolute area declined by 18.7% (46,233–37,603 km <jats:sup>2</jats:sup> ), shifting from water‐stressed Central Hebei to favorable regions in Northern Henan and Northwestern Shandong. Single maize expanded 10.8% (12,644–14,011 km <jats:sup>2</jats:sup> ), concentrated in groundwater‐depleted zones like Cangzhou, aligning with drought adaptation. Cotton cultivation plummeted (14.1%–3.3%) due to labor intensity and low profitability. Fruit/forest surged 164%, driven by policies and markets, whereas vegetables fragmented near urban areas. Transition analysis revealed 18% of WW‐SM areas converted to fruit/forest and vegetables during 2012–2017, coinciding with China's 2014 Groundwater Overdraft Control Regulation. Although adaptive strategies have mitigated water stress, persistent spatial mismatches in water allocation remain. Our integrated framework demonstrates robust capacity for agricultural landscape monitoring, providing actionable insights for the water‐energy‐food nexus.","PeriodicalId":203,"journal":{"name":"Land Degradation & Development","volume":"20 1","pages":""},"PeriodicalIF":4.7,"publicationDate":"2025-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145594148","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Zhenni Wu, Jantiene E. M. Baartman, João Pedro Nunes, Manuel López‐Vicente
Sediment connectivity between source areas and the main streams or local sinks is a complex and dynamic process, especially in large basins due to multiple heterogeneities and interactions between connectivity components. Sediment connectivity indices are promising tools to investigate sediment transport, especially in data‐scarce or large areas. The InVEST‐SDR numerical approach couples RUSLE gross erosion estimates with the Index of Connectivity (IC) to derive sediment delivery. However, involving functional connectivity and validating the results remains challenging. In a first estimate, we used the coupled InVEST‐SDR approach to calculate the annual sediment yield in the entire Wei River Basin (134,800 km 2 ) and three of its sub‐catchments. Then, we replaced the IC with the Aggregated Index of Connectivity (AIC), which includes functional connectivity aspects. Computational results were compared with observation data from 26 hydrometric stations to verify the performance of the simulations. Both IC and AIC performed well in predicting sediment yield, with R2 > 0.91. The areas with the highest connectivity (90th percentile—P90) also showed high values of erosion: 54% of the P90 values were found in the three catchments with the highest observed sediment yield. The rainfall erosivity and soil permeability factors were found to be the main explanatory components of the difference in spatial domination of structural (no temporary changes) and functional (temporally dynamic) connectivity. This study demonstrated the accuracy of AIC for sediment transport and yield evaluation in a large river basin. This method is potentially beneficial for land management in large basin areas with insufficient data.
{"title":"Evaluation of Sediment Connectivity Indices to Improve the Prediction of the Spatiotemporal Variability of Sediment Yield for a Large River Basin (Wei River, China)","authors":"Zhenni Wu, Jantiene E. M. Baartman, João Pedro Nunes, Manuel López‐Vicente","doi":"10.1002/ldr.70318","DOIUrl":"https://doi.org/10.1002/ldr.70318","url":null,"abstract":"Sediment connectivity between source areas and the main streams or local sinks is a complex and dynamic process, especially in large basins due to multiple heterogeneities and interactions between connectivity components. Sediment connectivity indices are promising tools to investigate sediment transport, especially in data‐scarce or large areas. The InVEST‐SDR numerical approach couples RUSLE gross erosion estimates with the Index of Connectivity (IC) to derive sediment delivery. However, involving functional connectivity and validating the results remains challenging. In a first estimate, we used the coupled InVEST‐SDR approach to calculate the annual sediment yield in the entire Wei River Basin (134,800 km <jats:sup>2</jats:sup> ) and three of its sub‐catchments. Then, we replaced the IC with the Aggregated Index of Connectivity (AIC), which includes functional connectivity aspects. Computational results were compared with observation data from 26 hydrometric stations to verify the performance of the simulations. Both IC and AIC performed well in predicting sediment yield, with <jats:italic>R</jats:italic> <jats:sup>2</jats:sup> > 0.91. The areas with the highest connectivity (90th percentile—P90) also showed high values of erosion: 54% of the P90 values were found in the three catchments with the highest observed sediment yield. The rainfall erosivity and soil permeability factors were found to be the main explanatory components of the difference in spatial domination of structural (no temporary changes) and functional (temporally dynamic) connectivity. This study demonstrated the accuracy of AIC for sediment transport and yield evaluation in a large river basin. This method is potentially beneficial for land management in large basin areas with insufficient data.","PeriodicalId":203,"journal":{"name":"Land Degradation & Development","volume":"5 1","pages":""},"PeriodicalIF":4.7,"publicationDate":"2025-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145583035","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Desertification, driven by climatic and anthropogenic factors, is one of the most pressing global environmental challenges, causing significant economic, ecological, and social consequences. A bibliometric analysis was performed to identify research trends and gaps in the desertification risk topic. Bibliometrix and Biblioshiny software were employed to analyse patterns in research publications. The analysis findings of the 864 research papers published between 1978 and 2024, sourced from the Scopus database, reveal a significant increase in desertification research with distinct development phases; geographically diverse contributions from China, Italy, Spain, and the United States; limited international collaboration; and high‐frequency keywords such as “climate change,” “risk assessment,” and “remote sensing.” Thematic evolution shows an early phase (1978–2001) on geographic information systems (GIS), remote sensing, and risk assessment; an expansion phase (2002–2020) on climate change; and a recent phase (2021–2024) marked by numerical and climate modeling. Three major research streams were identified: climatic drivers and climate change, technological advancements in monitoring and assessment, and socio‐economic and policy dimensions. The analysis reveals critical gaps including limited integration of socio‐economic data with climate models and underutilisation of artificial intelligence (AI) for monitoring desertification and land degradation. Future research should integrate models with socio‐economic data, leverage big data and AI, expand research to underrepresented regions, and scale community‐based solutions. Strengthening interdisciplinary collaboration will support adaptive, sustainable frameworks to combat desertification and foster resilience.
{"title":"Desertification Risk: Bibliometric Analysis and Future Research Directions","authors":"Fatima‐Ezzahrae Imam, Daniela Smiraglia, Antonio Pulina, Francesca Assennato, Elisabetta Raparelli, Giovanna Seddaiu","doi":"10.1002/ldr.70313","DOIUrl":"https://doi.org/10.1002/ldr.70313","url":null,"abstract":"Desertification, driven by climatic and anthropogenic factors, is one of the most pressing global environmental challenges, causing significant economic, ecological, and social consequences. A bibliometric analysis was performed to identify research trends and gaps in the desertification risk topic. Bibliometrix and Biblioshiny software were employed to analyse patterns in research publications. The analysis findings of the 864 research papers published between 1978 and 2024, sourced from the Scopus database, reveal a significant increase in desertification research with distinct development phases; geographically diverse contributions from China, Italy, Spain, and the United States; limited international collaboration; and high‐frequency keywords such as “climate change,” “risk assessment,” and “remote sensing.” Thematic evolution shows an early phase (1978–2001) on geographic information systems (GIS), remote sensing, and risk assessment; an expansion phase (2002–2020) on climate change; and a recent phase (2021–2024) marked by numerical and climate modeling. Three major research streams were identified: climatic drivers and climate change, technological advancements in monitoring and assessment, and socio‐economic and policy dimensions. The analysis reveals critical gaps including limited integration of socio‐economic data with climate models and underutilisation of artificial intelligence (AI) for monitoring desertification and land degradation. Future research should integrate models with socio‐economic data, leverage big data and AI, expand research to underrepresented regions, and scale community‐based solutions. Strengthening interdisciplinary collaboration will support adaptive, sustainable frameworks to combat desertification and foster resilience.","PeriodicalId":203,"journal":{"name":"Land Degradation & Development","volume":"77 1","pages":""},"PeriodicalIF":4.7,"publicationDate":"2025-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145567203","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mining activities reshape landscape patterns and degrade the eco‐environmental quality significantly. Multidimensional ecological monitoring and ecological zoning based on these two impacts in mining cities need further exploration. Taking Dexing City (DX) as the study area, this study constructed an integrated framework that combines a modified remote sensing ecological index (MRSEI), which incorporates PM2.5 and soil erosion, with landscape ecological risk (LER). We analyzed the spatiotemporal evolution and future trends of MRSEI and LER from 2000 to 2023 using trend analysis, the Mann–Kendall mutation test, and the Hurst exponent analysis, further explored their correlation, and delineated ecological zones (EZs) with a four‐quadrant model. The results showed that low‐MRSEI and high‐LER areas were concentrated in northern urban and mining zones. MRSEI in DX followed a “degradation–improvement” trend, whereas medium‐ and large‐scale mine sites displayed “degradation–improvement–degradation” fluctuations. MRSEI in urban and mining zones is predicted to continue deteriorating, whereas forests in the east and west may show slight improvements. LER rose persistently, especially in urban, agricultural, and mining zones, and acted as a key negative driver of MRSEI. Ecological zoning revealed a reduction in superior and poor EZs, and an expansion in good EZs. However, poor EZs within mine sites continued to grow, highlighting the sustained ecological pressure from mining. Overall, in mining cities, urban development and mining activities remain long‐term ecological hotspots. It is essential to implement differentiated strategies, prioritizing the conservation of natural ecosystems while enforcing stricter regulation and implementing adaptive restoration in mining zones.
{"title":"Dynamic Monitoring and Ecological Zoning Based on Eco‐Environmental Quality and Landscape Ecological Risk in Dexing City, China","authors":"Yian Chen, Baoqun Hu, Jianglong Tang, Yun Wang","doi":"10.1002/ldr.70310","DOIUrl":"https://doi.org/10.1002/ldr.70310","url":null,"abstract":"Mining activities reshape landscape patterns and degrade the eco‐environmental quality significantly. Multidimensional ecological monitoring and ecological zoning based on these two impacts in mining cities need further exploration. Taking Dexing City (DX) as the study area, this study constructed an integrated framework that combines a modified remote sensing ecological index (MRSEI), which incorporates PM2.5 and soil erosion, with landscape ecological risk (LER). We analyzed the spatiotemporal evolution and future trends of MRSEI and LER from 2000 to 2023 using trend analysis, the Mann–Kendall mutation test, and the Hurst exponent analysis, further explored their correlation, and delineated ecological zones (EZs) with a four‐quadrant model. The results showed that low‐MRSEI and high‐LER areas were concentrated in northern urban and mining zones. MRSEI in DX followed a “degradation–improvement” trend, whereas medium‐ and large‐scale mine sites displayed “degradation–improvement–degradation” fluctuations. MRSEI in urban and mining zones is predicted to continue deteriorating, whereas forests in the east and west may show slight improvements. LER rose persistently, especially in urban, agricultural, and mining zones, and acted as a key negative driver of MRSEI. Ecological zoning revealed a reduction in superior and poor EZs, and an expansion in good EZs. However, poor EZs within mine sites continued to grow, highlighting the sustained ecological pressure from mining. Overall, in mining cities, urban development and mining activities remain long‐term ecological hotspots. It is essential to implement differentiated strategies, prioritizing the conservation of natural ecosystems while enforcing stricter regulation and implementing adaptive restoration in mining zones.","PeriodicalId":203,"journal":{"name":"Land Degradation & Development","volume":"199 1","pages":""},"PeriodicalIF":4.7,"publicationDate":"2025-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145567246","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Nitrogen (N) fertilization is considered a powerful alternative approach to restore degraded alpine meadows. However, whether the form of available N matters and its efficiency along a degradation gradient remains largely unexplored. A four‐year N addition experiment with different available N forms (NH 4+ ‐N, NO 3− ‐N, and Glycine) was carried out on the undegraded, moderately (MD) and severely degraded (SD) alpine meadows on the Tibetan Plateau. Plant aboveground productivity was greatly increased in undegraded and MD alpine meadows but belowground productivity was enhanced in SD alpine meadow, especially in the two inorganic N treatments. When inorganic N was added, plants accumulated more N compared to the control in undegraded alpine meadow. Plants accumulated more N in the MD alpine meadow than in the undegraded alpine meadow in all forms of N addition treatments as the plant N uptake rate was almost doubled in the MD alpine meadow when N was added. In the SD alpine meadow, most of the added N moved to the microbial N pool with the largest increase observed in the NO 3− ‐N treatment. Leaching remained almost unchanged in NH 4+ ‐N and Glycine in undegraded and MD alpine meadows but increased in the SD alpine meadow, especially in the NO 3− ‐N treatment. However, leaching was low, accounting for less than 5% of the added N even in the SD alpine meadow. Our results indicate (1) the added N was mostly up taken and retained in plants or microorganisms regardless of N forms, (2) inorganic N is more efficient in increasing plant productivity for MD but not for SD alpine meadow, (3) fertilization using inorganic N to restore the degraded alpine meadow is efficient for MD but not for SD alpine meadows.
{"title":"Alpine Meadow Could Be Better Restored Before Moderate Degradation Using Inorganic Nitrogen on the Tibetan Plateau","authors":"Jianbo Sun, Chimin Lai, Fei Peng, Jun Zhou, Mengting Hu, Xingzhi Xu, Xiaowei Gou, Huakun Zhou, Carly Stevens","doi":"10.1002/ldr.70317","DOIUrl":"https://doi.org/10.1002/ldr.70317","url":null,"abstract":"Nitrogen (N) fertilization is considered a powerful alternative approach to restore degraded alpine meadows. However, whether the form of available N matters and its efficiency along a degradation gradient remains largely unexplored. A four‐year N addition experiment with different available N forms (NH <jats:sub>4</jats:sub> <jats:sup>+</jats:sup> ‐N, NO <jats:sub>3</jats:sub> <jats:sup>−</jats:sup> ‐N, and Glycine) was carried out on the undegraded, moderately (MD) and severely degraded (SD) alpine meadows on the Tibetan Plateau. Plant aboveground productivity was greatly increased in undegraded and MD alpine meadows but belowground productivity was enhanced in SD alpine meadow, especially in the two inorganic N treatments. When inorganic N was added, plants accumulated more N compared to the control in undegraded alpine meadow. Plants accumulated more N in the MD alpine meadow than in the undegraded alpine meadow in all forms of N addition treatments as the plant N uptake rate was almost doubled in the MD alpine meadow when N was added. In the SD alpine meadow, most of the added N moved to the microbial N pool with the largest increase observed in the NO <jats:sub>3</jats:sub> <jats:sup>−</jats:sup> ‐N treatment. Leaching remained almost unchanged in NH <jats:sub>4</jats:sub> <jats:sup>+</jats:sup> ‐N and Glycine in undegraded and MD alpine meadows but increased in the SD alpine meadow, especially in the NO <jats:sub>3</jats:sub> <jats:sup>−</jats:sup> ‐N treatment. However, leaching was low, accounting for less than 5% of the added N even in the SD alpine meadow. Our results indicate (1) the added N was mostly up taken and retained in plants or microorganisms regardless of N forms, (2) inorganic N is more efficient in increasing plant productivity for MD but not for SD alpine meadow, (3) fertilization using inorganic N to restore the degraded alpine meadow is efficient for MD but not for SD alpine meadows.","PeriodicalId":203,"journal":{"name":"Land Degradation & Development","volume":"165 1","pages":""},"PeriodicalIF":4.7,"publicationDate":"2025-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145567245","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Xin Huang, Hongwei Wang, Can Wang, Jinhua Wu, Suyan Yi
Managing cultivated land multifunction supply–demand (CLMSD) relationships and controlling certain drivers to maintain well‐matched cultivated land multifunction (CLM) combinations are key challenges in the sustainable use of cultivated land resources. Although methods have been established in the CLM research focusing on CLMSD matching, a complete understanding of the spatio‐temporal relationships and drivers of CLMSD mismatch is still lacking. Therefore, this study proposes a framework for assessing and managing CLMSD based on supply–demand bundles. This framework incorporates the bundle method and integrates spatio‐temporal analysis methods for CLMSD: quantifying the supply, demand, and supply–demand relationships of CLM at different periods; identifying CLMSD bundles using self‐organizing maps; analyzing the tradeoffs/synergies of CLMSD relationships in conjunction with supply–demand bundles; and exploring the driving factors using redundancy analysis. The results revealed that the supply–demand of the production function in Xinjiang remained relatively stable during the study period. The supply of ecological function increased by 42%, whereas its demand remained largely unchanged. Similarly, the supply of life function increased by 49%, whereas its demand remained relatively stable. Both the supply and demand for the landscape culture function indicated an upward trend, with increases of 18% and 138%, respectively. Overall, the CLMSD relationships were characterized by a surplus. Three CLMSD bundles were identified in the study area, and the CLMSD relationships and spatial locations of each bundle differed significantly. Bundle 1 was primarily located in the desert and was characterized by low supply and demand for CLM. Bundle 2 was concentrated in oasis cities and was characterized by the synergistic development of CLMSD relationships. Bundle 3 was situated near the Tianshan Mountains and was characterized by the highly synergistic development of CLMSD relationships. In addition, the direction of influence of the trade‐offs/synergies and drivers of the three types of bundles was not constant over time, and the degree of influence varied. Based on this, we propose different cultivated land management strategies. This study emphasizes the importance of considering the spatio‐temporal relationships and drivers of CLMSD mismatch when designing cultivated land protection and management strategies, and may serve as a reference for addressing the complex human–land relationship.
{"title":"Understanding the Spatio‐Temporal Relationships and Drivers of Cultivated Land Multifunction Supply–Demand Mismatch Towards Targeted Cultivated Land Management","authors":"Xin Huang, Hongwei Wang, Can Wang, Jinhua Wu, Suyan Yi","doi":"10.1002/ldr.70295","DOIUrl":"https://doi.org/10.1002/ldr.70295","url":null,"abstract":"Managing cultivated land multifunction supply–demand (CLMSD) relationships and controlling certain drivers to maintain well‐matched cultivated land multifunction (CLM) combinations are key challenges in the sustainable use of cultivated land resources. Although methods have been established in the CLM research focusing on CLMSD matching, a complete understanding of the spatio‐temporal relationships and drivers of CLMSD mismatch is still lacking. Therefore, this study proposes a framework for assessing and managing CLMSD based on supply–demand bundles. This framework incorporates the bundle method and integrates spatio‐temporal analysis methods for CLMSD: quantifying the supply, demand, and supply–demand relationships of CLM at different periods; identifying CLMSD bundles using self‐organizing maps; analyzing the tradeoffs/synergies of CLMSD relationships in conjunction with supply–demand bundles; and exploring the driving factors using redundancy analysis. The results revealed that the supply–demand of the production function in Xinjiang remained relatively stable during the study period. The supply of ecological function increased by 42%, whereas its demand remained largely unchanged. Similarly, the supply of life function increased by 49%, whereas its demand remained relatively stable. Both the supply and demand for the landscape culture function indicated an upward trend, with increases of 18% and 138%, respectively. Overall, the CLMSD relationships were characterized by a surplus. Three CLMSD bundles were identified in the study area, and the CLMSD relationships and spatial locations of each bundle differed significantly. Bundle 1 was primarily located in the desert and was characterized by low supply and demand for CLM. Bundle 2 was concentrated in oasis cities and was characterized by the synergistic development of CLMSD relationships. Bundle 3 was situated near the Tianshan Mountains and was characterized by the highly synergistic development of CLMSD relationships. In addition, the direction of influence of the trade‐offs/synergies and drivers of the three types of bundles was not constant over time, and the degree of influence varied. Based on this, we propose different cultivated land management strategies. This study emphasizes the importance of considering the spatio‐temporal relationships and drivers of CLMSD mismatch when designing cultivated land protection and management strategies, and may serve as a reference for addressing the complex human–land relationship.","PeriodicalId":203,"journal":{"name":"Land Degradation & Development","volume":"132 1","pages":""},"PeriodicalIF":4.7,"publicationDate":"2025-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145559447","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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